The Internet has afforded the possibility to innovate new products and services. One of the areas exposed via the Internet is that of recommendations, personalization and targeted advertising, collectively referred to herein as suggestions. Currently, there are many computer systems that support sophisticated suggestions.
In most instances a server makes suggestions available to a client. A server is typically a high throughput, fault tolerant combination of infrastructure, operating software and application software. The servers typically communicate with the client using common IP based protocols, for example, without limitation, hypertext transfer protocol (HTTP), hypertext transfer protocol secure (HTTPS), etc.
There are many approaches to identifying applicable suggestions. These approaches tend to be implemented by the following methods: by basing the suggestions upon analysis of tracked user behavior, or by allowing third parties, typically humans or other computer systems, to explicitly configure suggestions and/or suggestion associations. In each case, many algorithms exist for achieving the identification of said suggestions.
Currently known methods for the aggregation of suggestions originating from many sources describe a framework that provides recommendations sourced from a set of ‘plug-in’ recommendation producers. This framework is essentially a generic mechanism for serving suggestions to a connecting device. Other known methods describe an element of affiliation. However, these currently known methods do not address the more powerful suggestion targeting to be gained by tracking user behavior across affiliated sites.
In view of the foregoing, there is a need for improved techniques for providing a framework for identifying and providing suggestions that supports various advanced features such as, but not limited to, tracking user behavior over affiliated sites and enabling reselling of suggestions.
Unless otherwise indicated illustrations in the figures are not necessarily drawn to scale.